We view semantic parsing as a machine translation problem where \(Q\)  is translated to \(F\) . A popular approach is to treat \(Q\)  and \(F\)  as sequences of symbols, and apply neural sequence-to-sequence models, which are originally developed for machine translation [1]}.
However, training these models in a weakly supervised setting is challenging, and there is no guarantee that the formal queries generated by these translation models are always grammatical and readily executable over the KB.
